智能系统学报2016，Vol.11Issue(2)：172-179,8.DOI:10.11992/tis.201506024
基于信息反馈和改进适应度评价的人工蜂群算法
Artificial bee colony algorithm based on information feedback and an improved fitness value evaluation
摘要
Abstract
The artificial bee colony ( ABC ) algorithm converges slowly and easily gets stuck on local solutions;hence, an ABC algorithm based on information feedback and an improved fitness value evaluation is proposed. The algorithm first introduces a memory mechanism for individual components to feedback information to enhance its ca⁃pacity for population exploitation and to accelerate the convergence speed. Then, it adopts a new fitness function to increase the difference between individuals and to avoid premature convergence from failing to identify the best indi⁃vidual. Finally, the algorithm integrates an optimal nectar⁃source guidance mechanism into the knockout function to prevent the production of unexpected individuals. Experiments were conducted on standard functions and were com⁃pared with those with several typical improved ABCs. The results show that the improved algorithm accelerates the convergence rate and improves the solution accuracy.关键词
人工蜂群算法/群体智能/进化算法/函数优化/信息反馈Key words
artificial bee colony algorithm/swarm intelligence/evolutionary algorithm/function optimization/in-formation feedback分类
信息技术与安全科学引用本文复制引用
陈杰,沈艳霞,陆欣..基于信息反馈和改进适应度评价的人工蜂群算法[J].智能系统学报,2016,11(2):172-179,8.基金项目
国家自然科学基金项目（61573167）；高等学校博士学科点专项科研基金项目（20130093110011）；江苏省自然科学基金项目（ BK20141114）． （）
